class_docstr: A pytorch module that is a wrapper for FrozenCLIPEmbedder module. it enhances FrozenCLIPEmbedder, making it possible to
  have unlimited prompt length and assign weights to tokens in prompt.
docstr: Converts text into a tensor with this text's tokens' embeddings. Note that those are embeddings before they are passed through
  transformers. nvpt is used as a maximum length in tokens. If text produces less teokens than nvpt, only this many is returned.
docstr_tok: ["Converts","text","into","a","tensor","with","this","text","'s","tokens","'","embeddings",".","Note","that","those","are","embeddings","before","they","are","passed","through","transformers",".","nvpt","is","used","as","a","maximum","length","in","tokens",".","If","text","produces","less","teokens","than","nvpt",",","only","this","many","is","returned","."]
code: def encode_embedding_init_text(self, init_text, nvpt):
      """Converts text into a tensor with this text's tokens' embeddings. Note that those are embeddings before they are passed through
      transformers. nvpt is used as a maximum length in tokens. If text produces less teokens than nvpt, only this many is returned.
      """
  
      raise NotImplementedError
  
imports: import math
  from collections import namedtuple
  import torch
  from modules import prompt_parser, devices, sd_hijack
  from modules.shared import opts
label_desc: Use image node_id 6 for calling the FrozenCLIPEmbedderWithCustomWordsBase obj's underlying member method code with example usage: obj.encode_embedding_init_text(init_text, nvpt) without return types